ML Research Engineer, Data

Weave Robotics; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Weave Robotics in San Francisco, CA is building a data-driven robotics platform that learns from real homes and businesses. You’ll architect and operate data pipelines that transform terabytes of fleet data into training datasets used by our models, and you’ll influence sampling, curricula, and data quality for scalable learning.

Ideal candidates bring hands-on data engineering experience at scale, fluency with Python and strong software fundamentals, plus familiarity with robotics data

Qualifications

• Experience building data pipelines at scale.
• Ability to turn raw sensor data into training datasets.
• Strong Python and software engineering fundamentals.
• Familiarity with robotics data challenges.

Responsibilities

• Know the data: characterize coverage, drift, and spectral analysis.
• Find needles: dig bad data out of terabytes and automate catching it.
• Build the data lifecycle: curation, preprocessing, annotation, augmentation, versioning.
• Use models as instruments: embedding search and filtering signals.
• Improve models through data: collaborate with researchers on failures and curate datasets.
• Build eval datasets and benchmarks across tasks, environments, embodiments, and versions.

Skills

Data engineering

ML data pipelines

Python

C++

Analytics

Data debugging

Distributed storage

Tools

S3

GCS

Job description

Join Us, and Ship Robots

Weave was founded to build the robots we’d want to have in our own home. We believe the next generation of robotics will transform everyday life by enabling people to do more and to reclaim time to spend on what’s important.

Join Us, and Ship Robots

Weave was founded to build the robots we’d want to have in our own home. We believe the next generation of robotics will transform everyday life by enabling people to do more and to reclaim time to spend on what’s important.

We also believe robots are in a sense like any other product: to matter, they have to ship. Our robots are already operating in real homes and businesses, giving us the opportunity to rapidly improve from real-world experience. With a growing team, strong customer demand, and capital for expansion, we’re entering an exciting stage of growth—and we’re looking for people with exceptional talent and standards to help bring home robotics to millions of households.

The Role

Most robot learning research is graded on evals that don't survive contact with the field. Ours is graded by robots doing useful work in real homes and businesses, every day. Our fleet generates robot data at terabyte scale that’s ingested by training runs every week. Our models are only as good as what we feed them, and you’ll architect and build the pipeline that drives their behavior.

You’ll turn raw fleet data (video, proprioception, actions, sensor streams) into the datasets our models train on, and follow that data into the training loop: sampling ratios, data mixes, and curricula are decisions you’ll shape with the research team. The job is equal parts data engineering and data understanding: build the platform that processes millions of episodes and feeds them to training, and know the data well enough to say what correlates with good and bad model behavior.

Responsibilities

• Know the data: characterize coverage, redundancy, and drift: spectral analysis on time-series, distributional statistics, clustering over embeddings.
• Find the needles: dig bad data out of terabytes of episodes: bad trajectories, bad annotations, dropped frames, desynced streams, then automate the catch so it never gets through again.
• Build the data lifecycle: curation, preprocessing, annotation, augmentation, versioning, and the training-ingest formats and loaders that serve it at full throughput.
• Use models as instruments: embedding search to mine scenarios, model loss and disagreement as quality signals, VLM-assisted filtering and labeling.
• Improve models through data: partner with researchers on model failures, then build the datasets, processing steps and samplers that target specific capabilities and failure modes, and own the sampling and mixture decisions that go into each run.
• Build the eval datasets and benchmarks that measure performance across tasks, environments, embodiments, and model versions.

What You\'ll Bring

• Data engineering at scale: pipelines over terabytes, object stores (S3, GCS), distributed storage, and indexing.
• Training ingest: high-throughput formats and dataloaders.
• Working ML experience: you can launch a fine-tune, read a loss curve, and design an ablation to test a data hypothesis.
• Analytical range: signal processing and statistics on real sensor data, and unsupervised structure-finding (PCA, UMAP, clustering) when the labels don't exist yet.
• Data debugging: you can trace a problem from sensor drift through a corrupted episode to a pipeline failure.
• Strong Python and software engineering fundamentals. C++ is a plus.

Nice to Have

• Batch processing at scale: Ray, Spark, or Dask over terabytes, with cost and throughput judgment.
• Workflow orchestration in production: Airflow, Kubeflow, or similar, with retries and monitoring.
• Robotics data pitfalls: timestamps, clock domains, and sensor modality quirks.
• Robot learning exposure: you’ve trained policies (VLAs, world models, RL) and can tell a data problem from a model problem.